AbstractAs robots become more prevalent in smaller manufacturing and maintenance settings, it will become important to enable them to learn new tasks quickly without explicit programming by a human. One particularly challenging domain in robot learning is handling nonrigid objects and materials such as fluids and easily deformable parts and tools. This talk explores this problem in the context of two robot tasks: pouring a specific volume of fluid into a moving container, and cleaning stains off of compliant objects. The fluid pouring task is used in development of a general approach for autonomous learning of trajectory parameters. As more data is acquired, the robot’s task performance improves substantially and it is able to very quickly find solutions to new task variations. The second task focuses on having the robot learn the deformation model of a compliant part. The deformation model uses a finite-element structure to represent the part, and learns the model by updating the stiffness parameters. When given a new part, the system only needs a few trials to improve quickly enough to clean new stains efficiently by predicting how much the part will deform under cleaning force.

About the Robotics Graduate Student Seminars

The Robotics Graduate Student Seminars at the University of Maryland College Park are a student-run series of talks given by current graduate students.

The purpose of these talks is to:

Encourage interaction between Robotics students from different subfields;

Provide an opportunity for Robotics students to be aware of and possibly get involved in the research their peers are conducting;

Provide an opportunity for Robotics students to receive feedback on their current research;